From 938dfcbe5ef119abc3925899a4ec5062e43505c3 Mon Sep 17 00:00:00 2001 From: Alex Zvoleff Date: Thu, 25 Jul 2024 13:28:47 -0400 Subject: [PATCH] Update datasets --- LDMP/data/gee_datasets.json | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/LDMP/data/gee_datasets.json b/LDMP/data/gee_datasets.json index 2b3fce1b9..332e19567 100644 --- a/LDMP/data/gee_datasets.json +++ b/LDMP/data/gee_datasets.json @@ -158,7 +158,7 @@ "MOD16A2": { "Data source": "GEE", "Start year": 2000, - "End year": 2021, + "End year": 2023, "Spatial Resolution": "1 km", "Temporal resolution": "annual", "Min Latitude": "-90", @@ -168,7 +168,7 @@ "Description": "The MOD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an 8-day composite product produced at 500 meter pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.", "Units": "Annual ET km/m2 (=mm) * 10", "Link to Dataset": "http://files.ntsg.umt.edu/data/NTSG_Products/MOD16/", - "GEE Dataset": "users/geflanddegradation/toolbox_datasets/et_modis_2000_2021", + "GEE Dataset": "users/geflanddegradation/toolbox_datasets/et_modis_2000_2023", "Source code": "c03_et_modis_int_global_assets", "License": "Public Domain", "License URL": "https://creativecommons.org/publicdomain/zero/1.0/", @@ -181,7 +181,7 @@ "ESA CCI": { "Data source": "ESA", "Start year": 1992, - "End year": 2020, + "End year": 2022, "Spatial Resolution": "300 m", "Temporal resolution": "annual", "Min Latitude": "-90", @@ -191,7 +191,7 @@ "Units": "Land cover classes", "Description": "The CCI-LC project delivers consistent global LC maps at 300 m spatial resolution on an annual basis from 1992 to 2018. The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.", "Link to Dataset": "http://maps.elie.ucl.ac.be/CCI/viewer/index.php", - "GEE Dataset": "users/geflanddegradation/toolbox_datasets/lcov_esacc_1992_2020", + "GEE Dataset": "users/geflanddegradation/toolbox_datasets/lcov_esacc_1992_2022", "Source code": "c04_esa_cci_landcover_global_assets", "License": "CC by-SA 3.0", "License URL": "https://creativecommons.org/licenses/by-sa/3.0/igo/", @@ -225,7 +225,7 @@ "MODIS (MOD13Q1, annual)": { "Data source": "GEE", "Start year": 2001, - "End year": 2021, + "End year": 2023, "Spatial Resolution": "250 m", "Temporal resolution": "annual", "Min Latitude": "-90", @@ -235,7 +235,7 @@ "Units": "Mean anual NDVI * 10000", "Description": "The MOD13Q1 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.", "Link to Dataset": "https://explorer.earthengine.google.com/#detail/MODIS%2FMOD13Q1", - "GEE Dataset": "users/geflanddegradation/toolbox_datasets/ndvi_modis_2001_2021", + "GEE Dataset": "users/geflanddegradation/toolbox_datasets/ndvi_modis_2001_2023", "Source code": "c02_ndvi_modis_int_global_assets", "License": "Public Domain", "License URL": "https://creativecommons.org/publicdomain/zero/1.0/", @@ -398,7 +398,7 @@ "CHIRPS": { "Data source": "GEE", "Start year": 1981, - "End year": 2021, + "End year": 2023, "Spatial resolution": "5 km", "Temporal resolution": "annual", "Min Latitude": "-50", @@ -408,7 +408,7 @@ "Units": "mm/year", "Description": "Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.", "Link to Dataset": "http://chg.geog.ucsb.edu/data/chirps/", - "GEE Dataset": "users/geflanddegradation/toolbox_datasets/prec_chirps_1981_2021", + "GEE Dataset": "users/geflanddegradation/toolbox_datasets/prec_chirps_1981_2023", "Source code": "c05_precip_chirps_int_global_assets", "License": "Public Domain", "License URL": "https://creativecommons.org/publicdomain/zero/1.0/", @@ -461,7 +461,7 @@ "PERSIANN-CDR": { "Data source": "GEE", "Start year": 1983, - "End year": 2018, + "End year": 2023, "Spatial resolution": "25 km", "Temporal resolution": "annual", "Min Latitude": "-60", @@ -471,7 +471,7 @@ "Units": "mm/year", "Description": "The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR) provides daily rainfall estimates at a spatial resolution of 0.25 degrees in the latitude band 60S - 60N from 1983 to the near-present. The precipitation estimate is produced using the PERSIANN algorithm on GridSat-B1 infrared satellite data, and the training of the artificial neural network is done using the National Centers for Environmental Prediction (NCEP) stage IV hourly precipitation data. The PERSIANN-CDR is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product version 2.2 (GPCPv2.2), so that the PERSIANN-CDR monthly means degraded to 2.5 degree resolution match GPCPv2.2. PERSIANN CDR is a Climate Data Record, which the National Research Council (NRC) defines as a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. ", "Link to Dataset": "https://climatedataguide.ucar.edu/climate-data/persiann-cdr-precipitation-estimation-remotely-sensed-information-using-artificial", - "GEE Dataset": "users/geflanddegradation/toolbox_datasets/prec_persian_1983_2018", + "GEE Dataset": "users/geflanddegradation/toolbox_datasets/prec_persian_1983_2023", "Source code": "c05_precip_presian_int_global_assets", "License": "Public Domain", "License URL": "https://creativecommons.org/publicdomain/zero/1.0/", @@ -505,7 +505,7 @@ "MERRA 2": { "Data source": "https://disc.sci.gsfc.nasa.gov/datasets?page=1&keywords=M2TMNXLND_5.12.4", "Start year": 1980, - "End year": 2016, + "End year": 2019, "Spatial resolution": "5 km", "Temporal resolution": "annual", "Min Latitude": "-90", @@ -597,7 +597,7 @@ "Hansen": { "Data source": "http://earthenginepartners.appspot.com/science-2013-global-forest", "Start year": 2000, - "End year": 2020, + "End year": 2023, "Spatial resolution": "30 m", "Temporal resolution": "annual", "Min Latitude": -57, @@ -607,7 +607,7 @@ "Units": "Percent tree cover", "Description": "Results from time-series analysis of Landsat images characterizing forest extent and change.Trees are defined as vegetation taller than 5m in height and are expressed as a percentage per output grid cell as ‘2000 Percent Tree Cover’. ‘Forest Cover Loss’ is defined as a stand-replacement disturbance, or a change from a forest to non-forest state, during the period 2000–2019. ‘Forest Cover Gain’ is defined as the inverse of loss, or a non-forest to forest change entirely within the period 2000–2012. ‘Forest Loss Year’ is a disaggregation of total ‘Forest Loss’ to annual time scales. Reference 2000 and 2019 imagery are median observations from a set of quality assessment-passed growing season observations.", "Link to Dataset": "http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html", - "GEE Dataset": "UMD/hansen/global_forest_change_2020_v1_8", + "GEE Dataset": "UMD/hansen/global_forest_change_2023_v1_11", "License": "CC by-SA 4.0", "License URL": "https://creativecommons.org/licenses/by/4.0/", "Source": "Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53",